Awesome-Document-Layout-Analysis

A curated list of resources dedicated to document layout analysis

1. Papers

• *CODE means official code and CODE means not official code

Conf.	Date	Title	Highlight	code/cite
ICDAR2021	2021/5/13	VSR: A Unified Framework for Document Layout Analysis combining Vision, Semantics and Relations	V,S,R	*CODE
KDD 2020	2020/6/16	LayoutLM: Pre-training of Text and Layout for Document Image Understanding	multimodal/pretrain	*CODE
ACL2021	2022/1/10	LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding	multimodal/pretrain	*CODE

Conf.	Date	Title	Highlight	code/cite
KDD2018	2018/5/24	Corpus Conversion Service: A Machine Learning Platform to Ingest Documents at Scale	pipeline	none

Conf.	Date	Title	Highlight	code/cite
ICDAR2017	2017/11/9	DeepDeSRT: Deep Learning for Detection and Structure Recognition of Tables in Document Images	table det	193

Conf.	Date	Title	Highlight	code/cite
ITC-irst Technical Report	1998	Geometric layout analysis techniques for document image understanding: a review	traditional	216
IWDAS	2002/8/19	Two geometric algorithms for layout analysis	traditional	231
PSDIUT	2003	High performance document layout analysis	traditional	139

2. Datasets

2.1 Introduction

Dataset	Description	dataset link
PubLayNet	PubLayNet is a large dataset of document images, of which the layout is annotated with both bounding boxes and polygonal segmentations.The annotations are automatically generated by matching the PDF format and the XML format.The size of the dataset is comparable to established computer vision datasets, containing over 360 thousand document images, where typical document layout elements are annotated.	PubLayNet
DocBank	DocBank is a new large-scale dataset that is constructed using a weak supervision approach. It enables models to integrate both the textual and layout information for downstream tasks. The current DocBank dataset totally includes 500K document pages, where 400K for training, 50K for validation and 50K for testing.	DocBank

2.2 Comparison of datasets for table structure recognition

TO DO

3. Other technical solutions

3.1 Relevant research institutions and scholars

• document AI

  • Minghao Li
  • Yiheng Xu

3.2 Related competitions

• ICDAR 2021 Competition on Scientific Literature Parsing (Task A on Document Layout Recognition)

  • First Place Method: Davar-Lab-OCR: VSR

3.3 Related lecture

• 2021

  • MSR: Document AI: Benchmarks, Models and Applications (Presentation@ICDAR 2021)